University of Helsinki, Faculty of Biological and Environmental Sciences, Department of Biosciences

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Institute of Biotechnology

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Ta, Hung

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2012-10-02T09:03:14Z

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2012-11-20

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2012-10-02T09:03:14Z

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2012-11-30

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URN:ISBN:978-952-10-8300-6

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http://hdl.handle.net/10138/37009

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Living systems, which are composed of biological components such as molecules, cells, organisms or entire species, are dynamic and complex. Their behaviors are difficult to study with respect to the properties of individual elements. To study their behaviors, we use quantitative techniques in the "omic" fields such as genomics, bioinformatics and proteomics to measure the behavior of groups of interacting components, and we use mathematical and computational modeling to describe and predict their dynamical behavior.
The first step in the understanding of a biological system is to investigate how its individual elements interact with each other. This step consist of drawing a static wiring diagram that connects the individual parts. Experimental techniques that are used - are designed to observe interactions among the biological components in the laboratory while computational approaches are designed to predict interactions among the individual elements based on their properties. In the first part of this thesis, we present techniques for network inference that are particularly targeted at protein-protein interaction networks. These techniques include comparative genomics, structure-based, biological context methods and integrated frameworks. We evaluate and compare the prediction methods that have been most often used for domain-domain interactions and we discuss the limitations of the methods and data resources. We introduce the concept of the Enhanced Phylogenetic Tree, which is a new graphical presentation of the evolutionary history of protein families; then, we propose a novel method for assigning functional linkages to proteins. This method was applied to predicting both human and yeast protein functional linkages.
The next step is to obtain insights into the dynamical aspects of the biological systems. One of the outreaching goals of systems biology is to understand the emergent properties of living systems, i.e., to understand how the individual components of a system come together to form distinct, collective and interactive properties and functions. The emergent properties of a system are neither to be found in nor are directly deducible from the lower-level properties of that system. An example of the emergent properties is synchronization, a dynamical state of complex network systems in which the individual components of the systems behave coherently, almost in unison. In the second part of the thesis, we apply computational modeling to mimic and simplify real-life complex systems. We focus on clarifying how the network topology determines the initiation and propagation of synchronization. A simple but efficient method is proposed to reconstruct network structures from functional behaviors for oscillatory systems such as brain. We study the feasibility of network reconstruction systematically for different regimes of coupling and for different network topologies. We utilize the Kuramoto model, an interacting system of oscillators, which is simple but relevant enough to address our questions.